Investigating the Effect of Olive Husk Ash on the Properties of Asphalt Concrete Mixture
Bibliographic record
Abstract
Technology in transportation used available resources to make it safe, fast, suitable, easy, economic, and environmental to transport people and goods. Olive Husk became an environmental problem as waste materials especially in the Middle East where huge quantities are found. The objective of this research is to investigate the effect of addition of Olive Husk Ash (OHA) on the properties of asphalt concrete mixtures. Marshall Test was used to perform the asphalt concrete mixture by the addition of OHA to the binder of asphalt; different percentages of OHA (0, 5, 10, 15, and 20%) by volume were added to the binder. Five percent of asphalt cements (5, 5.5, 6, 6.5 and 7%) by weight and limestone aggregate were used for preparing asphalt mixture specimens to find the optimum content of asphalt that could be used in the binder. Tests on flow, stability, air void percentage and void in mineral aggregate, retained stability, stiffness, and retained stiffness were made. The principle results on OHA as filler in Asphalt binder improves the Marshall Stability, and void in mineral aggregate and decrease in flow, retained stability, stiffness, and retained stiffness with a 10%-15% of olive husk ash replacement of asphalt binder. The contribution that OHA could be used as a pavement construction material in field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".